About Me


Welcome! I am Malika Nisal Ratnayake, a Research Fellow at the Computational and Collective Intelligence group within the Department of Data Science and AI at Monash University, Australia. My research interests lie at the intersection of computer vision, artificial intelligence, and ecology, with a specific focus on advancing pollination monitoring in agriculture.


I recently completed my doctorate in Computer Science at Monash University, under the supervision of Associate Professor Alan Dorin and Associate Professor Adrian Dyer. During my doctoral research, I designed computer vision and deep learning frameworks for automated pollination monitoring. A significant aspect of my work involved collaborating with farms to develop and implement pollination monitoring systems that met the industry's specific requirements. I received the prestigious Green Talents Award for Outstanding Young Scientists in Sustainable Development from the German Federal Ministry of Education and Research, in recognition of the high impact of my research on global sustainability. Currently, I am working as a Research Fellow on a collaborative project with the industry to enhance the sustainability and competitiveness of the Australian agricultural sector through the automation and data-driven transformation of industrial crop pollination.

Publications


Talks and Presentations


Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination

Invited poster presentation at "CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling" workshop held in conjunction with Computer Vision and Pattern Recognition 2022
Workshop page | Poster | Paper

Tracking Honey Bees with Machine Vision and Artificial Intelligence

Invited Talk at the Central Association of Bee Keepers (CABK), United Kingdom (February 2022)
Event page | Youtube Video

Spatial Modelling of Cross-pollination in Crop Systems Through Multi-point Honeybee Tracking

Oral presentation at the 24th International Congress on Modelling and Simulation (MODSIM2021)
Conference page | Youtube Video | Paper
Watch the oral presentation

Towards Computer Vision and Deep Learning Facilitated Pollination Monitoring for Agriculture

Oral and Poster presentation at the "2nd Agriculture-Vision: Challenges & Oppotunities for Computer Vision in Agriculture" workshop held in conjunction with Computer Vision and Pattern Recognition 2021
Workshop page | Youtube Video | Poster | Paper
Watch the oral presentation

Tracking Individual Honeybees among Wildflower Clusters with Computer Vision-facilitated Pollinator Monitoring

Poster presentation at "CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling" workshop held in conjunction with Computer Vision and Pattern Recognition 2021
Workshop page | Poster | Paper
Watch the oral presentation

Decoding Bees with Video Recordings

Submission for the Visualise Your Thesis Competition (2020)
Video Link

Software


Datasets